AI can't replace cybersecurity experts yet, experts say

Artificial intelligence is making significant inroads into the cybersecurity sector, offering capabilities that range from writing code to analyzing source code and scanning for vulnerabilities. This rapid integration has naturally led to questions about whether AI will eventually replace human cybersecurity professionals. However, industry experts emphasize that despite AI's advancements, critical gaps remain.

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AI can understand what the code writes, but it's difficult to truly understand why it was designed this way.

One major limitation is AI's understanding of context and real-world application. When analyzing large, complex codebases, especially those with intricate system architectures, AI can struggle to maintain a coherent understanding of the interactions between different modules. This can lead to misinterpretations, where normal designs are flagged as vulnerabilities or, conversely, genuine risks are overlooked. The cost of such errors, including false positives requiring extensive human verification, can be substantial.

Furthermore, AI's reliance on code comments presents another challenge. While AI analyzes source code, it also considers developer annotations. If these comments are inaccurate, outdated, or intentionally misleading, AI may make incorrect security judgments. Unlike AI, human cybersecurity experts verify actual program execution rather than solely trusting developer notes.

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AI's understanding of actual usage scenarios is still insufficient.

Perhaps the most significant hurdle is AI's inability to grasp the underlying design intent and business logic of a system. While AI can trace the flow of code execution, it often fails to understand *why* a particular design was chosen or whether it aligns with the company's operational rules. This is crucial for identifying vulnerabilities that arise not from syntax errors but from flaws in the overall business logic, such as in payment or membership systems.

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AI's over-reliance on code comments.

Despite these limitations, AI is poised to become an indispensable tool for cybersecurity teams. It can automate large-scale reconnaissance, identify an organization's external attack surface, and process test results, thereby reducing the burden of repetitive tasks. However, the strategic aspects of cybersecurity, such as determining attack targets, timing, and stealth, will continue to rely on human expertise and experience. AI is best positioned as a powerful assistant, enabling human experts to focus on higher-level strategy, novel research, and in-depth analysis.

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AI finds it difficult to understand the system's true design purpose and business logic.